Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation.

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein's unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Biomechanical Assessment of Liver Integrity: Prospective Evaluation of Mechanical Versus Acoustic MR Elastography.

BACKGROUND: Magnetic resonance elastography (MRE) can quantify tissue biomechanics noninvasively, including pathological hepatic states like metabolic dysfunction-associated steatohepatitis. PURPOSE: To compare the performance of 2D/3D-MRE using the gravitational (GT) transducer concept with the current commercial acoustic (AC) solution utilizing a 2D-MRE approach. Additionally, quality index markers (QIs) were proposed to identify image pixels with sufficient quality for reliably estimating tissue biomechanics. STUDY TYPE: Prospective. POPULATION: One hundred seventy participants with suspected or confirmed liver disease (median age, 57 years [interquartile range (IQR), 46-65]; 66 females), and 11 healthy volunteers (median age, 31 years [IQR, 27-34]; 5 females). FIELD STRENGTH/SEQUENCE: Participants were scanned twice at 1.5 T and 60 Hz vibration frequency: first, using AC-MRE (2D-MRE, spin-echo EPI sequence, 11 seconds breath-hold), and second, using GT-MRE (2D- and 3D-MRE, gradient-echo sequence, 14 seconds breath-hold). ASSESSMENT: Image analysis was performed by four independent radiologists and one biomedical engineer. Additionally, superimposed analytic plane shear waves of known wavelength and attenuation at fixed shear modulus were used to propose pertinent QIs. STATISTICAL TESTS: Spearman's correlation coefficient (r) was applied to assess the correlation between modalities. Interreader reproducibility was evaluated using Bland-Altman bias and reproducibility coefficients. P-values <0.05 were considered statistically significant. RESULTS: Liver stiffness quantified via GT-2D/3D correlated well with AC-2D (r ≥ 0.89 [95% CI: 0.85-0.92]) and histopathological grading (r ≥ 0.84 [95% CI: 0.72-0.91]), demonstrating excellent agreement in Bland-Altman plots and between readers (κ ≥ 0.86 [95% CI: 0.81-0.91]). However, GT-2D showed a bias in overestimating stiffness compared to GT-3D. Proposed QIs enabled the identification of pixels deviating beyond 10% from true stiffness based on a combination of total wave amplitude, temporal sinusoidal nonlinearity, and wave signal-to-noise ratio for GT-3D. CONCLUSION: GT-MRE represents an alternative to AC-MRE for noninvasive liver tissue characterization. Both GT-2D and 3D approaches correlated strongly with the established commercial approach, offering advanced capabilities in abdominal imaging compared to AC-MRE. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Making sense of scattering: Seeing microstructure through shear waves.

The physics of shear waves traveling through matter carries fundamental insights into its structure, for instance, quantifying stiffness for disease characterization. However, the origin of shear wave attenuation in tissue is currently not properly understood. Attenuation is caused by two phenomena: absorption due to energy dissipation and scattering on structures such as vessels fundamentally tied to the material's microstructure. Here, we present a scattering theory in conjunction with magnetic resonance imaging, which enables the unraveling of a material's innate constitutive and scattering characteristics. By overcoming a three-order-of-magnitude scale difference between wavelength and average intervessel distance, we provide noninvasively a macroscopic measure of vascular architecture. The validity of the theory is demonstrated through simulations, phantoms, in vivo mice, and human experiments and compared against histology as gold standard. Our approach expands the field of imaging by using the dispersion properties of shear waves as macroscopic observable proxies for deciphering the underlying ultrastructures.

Magnetic resonance elastography resolving all gross anatomical segments of the kidney during controlled hydration.

Introduction: Magnetic resonance elastography (MRE) is a non-invasive method to quantify biomechanical properties of human tissues. It has potential in diagnosis and monitoring of kidney disease, if established in clinical practice. The interplay of flow and volume changes in renal vessels, tubule, urinary collection system and interstitium is complex, but physiological ranges of in vivo viscoelastic properties during fasting and hydration have never been investigated in all gross anatomical segments simultaneously. Method: Ten healthy volunteers underwent two imaging sessions, one following a 12-hour fasting period and the second after a drinking challenge of >10 mL per kg body weight (60-75 min before the second examination). High-resolution renal MRE was performed using a novel driver with rotating eccentric mass placed at the posterior-lateral wall to couple waves (50 Hz) to the kidney. The biomechanical parameters, shear wave speed (cs in m/s), storage modulus (Gd in kPa), loss modulus (Gl in kPa), phase angle (Υ=2πatanGlGd) and attenuation (α in 1/mm) were derived. Accurate separation of gross anatomical segments was applied in post-processing (whole kidney, cortex, medulla, sinus, vessel). Results: High-quality shear waves coupled into all gross anatomical segments of the kidney (mean shear wave displacement: 163 ± 47 µm, mean contamination of second upper harmonics <23%, curl/divergence: 4.3 ± 0.8). Regardless of the hydration state, median Gd of the cortex and medulla (0.68 ± 0.11 kPa) was significantly higher than that of the sinus and vessels (0.48 ± 0.06 kPa), and consistently, significant differences were found in cs, Υ, and Gl (all p < 0.001). The viscoelastic parameters of cortex and medulla were not significantly different. After hydration sinus exhibited a small but significant reduction in median Gd by -0.02 ± 0.04 kPa (p = 0.01), and, consequently, the cortico-sinusoidal-difference in Gd increased by 0.04 ± 0.07 kPa (p = 0.05). Only upon hydration, the attenuation in vessels became lower (0.084 ± 0.013 1/mm) and differed significantly from the whole kidney (0.095 ± 0.007 1/mm, p = 0.01). Conclusion: High-resolution renal MRE with an innovative driver and well-defined 3D segmentation can resolve all renal segments, especially when including the sinus in the analysis. Even after a prolonged hydration period the approach is sensitive to small hydration-related changes in the sinus and in the cortico-sinusoidal-difference.

BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing.

In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain.

Myco-Synthesis of Silver Nanoparticles and Their Bioactive Role against Pathogenic Microbes.

Nanotechnology based on nanoscale materials is rapidly being used in clinical settings, particularly as a new approach for infectious illnesses. Recently, many physical/chemical approaches utilized to produce nanoparticles are expensive and highly unsafe to biological species and ecosystems. This study demonstrated an environmentally friendly mode of producing nanoparticles (NPs) where Fusarium oxysporum has been employed for generation of silver nanoparticles (AgNPs), which were further tested for their antimicrobial potentials against a variety of pathogenic microorganisms. The characterization of NPs was completed by UV-Vis spectroscopy, DLS and TEM, where it has been found that the NPs were mostly globular, with the size range of 50 to 100 nm. The myco-synthesized AgNPs showed prominent antibacterial potency observed as zone of inhibition of 2.6 mm, 1.8 mm, 1.5 mm, and 1.8 mm against Vibrio cholerae, Streptococcus pneumoniae, Klebsiella pneumoniae and Bacillus anthracis, respectively, at 100 µM. Similarly, at 200 µM for A. alternata, A. flavus and Trichoderma have shown zone of inhibition as 2.6 mm, 2.4 mm, and 2.1 mm, respectively. Moreover, SEM analysis of A. alternata confirmed the hyphal damage where the layers of membranes were torn off, and further EDX data analysis showed the presence of silver NPs, which might be responsible for hyphal damage. The potency of NPs may be related with the capping of fungal proteins that are produced extracellularly. Thus, these AgNPs may be used against pathogenic microbes and play a beneficial role against multi-drug resistance.

A randomized study to compare oral potassium binders in the treatment of acute hyperkalemia.

BACKGROUND: The KBindER (K+ Binders in Emergency Room and hospitalized patients) clinical trial is the first head-to-head evaluation of oral potassium binders (cation-exchange resins) for acute hyperkalemia therapy. METHODS: Emergency room and hospitalized patients with a blood potassium level ≥ 5.5 mEq/L are randomized to one of four study groups: potassium binder drug (sodium polystyrene sulfonate, patiromer, or sodium zirconium cyclosilicate) or nonspecific laxative (polyethylene glycol). Exclusion criteria include recent bowel surgery, ileus, diabetic ketoacidosis, or anticipated dialysis treatment within 4 h of treatment drug. Primary endpoints include change in potassium level at 2 and 4 h after treatment drug. Length of hospital stay, next-morning potassium level, gastrointestinal side effects and palatability will also be analyzed. We are aiming for a final cohort of 80 patients with complete data endpoints (20 per group) for comparative statistics including multivariate adjustment for kidney function, diabetes mellitus, congestive heart failure, metabolic acidosis, renin-angiotensin-aldosterone system inhibitor prescription, and treatment with other agents to lower potassium (insulin, albuterol, loop diuretics). DISCUSSION: The findings from our study will inform decision-making guidelines on the role of oral potassium binders in the treatment of acute hyperkalemia. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04585542 . Registered 14 October 2020.

Single Breath-Hold 3-Dimensional Magnetic Resonance Elastography Depicts Liver Fibrosis and Inflammation in Obese Patients.

OBJECTIVES: Three-dimensional (3D) magnetic resonance elastography (MRE) measures liver fibrosis and inflammation but requires several breath-holds that hamper clinical acceptance. The aim of this study was to evaluate the technical and clinical feasibility of a single breath-hold 3D MRE sequence as a means of measuring liver fibrosis and inflammation in obese patients. METHODS: From November 2020 to December 2021, subjects were prospectively enrolled and divided into 2 groups. Group 1 included healthy volunteers (n = 10) who served as controls to compare the single breath-hold 3D MRE sequence with a multiple-breath-hold 3D MRE sequence. Group 2 included liver patients (n = 10) who served as participants to evaluate the clinical feasibility of the single breath-hold 3D MRE sequence in measuring liver fibrosis and inflammation. Controls and participants were scanned at 60 Hz mechanical excitation with the single breath-hold 3D MRE sequence to retrieve the magnitude of the complex-valued shear modulus (|G*| [kPa]), the shear wave speed (Cs [m/s]), and the loss modulus (G" [kPa]). The controls were also scanned with a multiple-breath-hold 3D MRE sequence for comparison, and the participants had histopathology (Ishak scores) for correlation with Cs and G". RESULTS: For the 10 controls, 5 were female, and the mean age and body mass index were 33.1 ± 9.5 years and 23.0 ± 2.1 kg/m 2 , respectively. For the 10 participants, 8 were female, and the mean age and body mass index were 45.1 ± 16.5 years and 33.1 ± 4.0 kg/m 2 (obese range), respectively. All participants were suspected of having nonalcoholic fatty liver disease. Bland-Altman analysis of the comparison in controls shows there are nonsignificant differences in |G*|, Cs, and G" below 6.5%, suggesting good consensus between the 2 sequences. For the participants, Cs and G" correlated significantly with Ishak fibrosis and inflammation grades, respectively ( ρ = 0.95, P < 0.001, and ρ = 0.84, P = 0.002). CONCLUSION: The single breath-hold 3D MRE sequence may be effective in measuring liver fibrosis and inflammation in obese patients.

Rechallenging nivolumab following immune checkpoint inhibitor-induced pericarditis.

We report a case of recurrent pericarditis as an immune-related adverse event in a 47-year-old man with de novo metastatic renal cell carcinoma. After first-line treatment with sunitinib failed, he received three cycles of nivolumab and developed pericarditis following each cycle. The third cycle was accompanied by colchicine as a secondary prophylaxis. Pericarditis is an uncommon and potentially life-threatening immune-related adverse event, if not managed promptly.

A survey of uncover misleading and cyberbullying on social media for public health.

Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

Estrogen modulation of the pronociceptive effects of serotonin on female rat trigeminal sensory neurons is timing dependent and dosage dependent and requires estrogen receptor alpha.

ABSTRACT: The role of the major estrogen estradiol (E2) on orofacial pain conditions remains controversial with studies reporting both a pronociceptive and antinociceptive role of E2. E2 modulation of peripheral serotonergic activity may be one mechanism underlying the female prevalence of orofacial pain disorders. We recently reported that female rats in proestrus and estrus exhibit greater serotonin (5HT)-evoked orofacial nocifensive behaviors compared with diestrus and male rats. Further coexpression of 5HT 2A receptor mRNA in nociceptive trigeminal sensory neurons that express transient receptor potential vanilloid 1 ion channels contributes to pain sensitization. E2 may exacerbate orofacial pain through 5HT-sensitive trigeminal nociceptors, but whether low or high E2 contributes to orofacial pain and by what mechanism remains unclear. We hypothesized that steady-state exposure to a proestrus level of E2 exacerbates 5HT-evoked orofacial nocifensive behaviors in female rats, explored the transcriptome of E2-treated female rats, and determined which E2 receptor contributes to sensitization of female trigeminal sensory neurons. We report that a diestrus level of E2 is protective against 5HT-evoked orofacial pain behaviors, which increase with increasing E2 concentrations, and that E2 differentially alters several pain genes in the trigeminal ganglia. Furthermore, E2 receptors coexpressed with 5HT 2A and transient receptor potential vanilloid 1 and enhanced capsaicin-evoked signaling in the trigeminal ganglia through estrogen receptor α. Overall, our data indicate that low, but not high, physiological levels of E2 protect against orofacial pain, and we provide evidence that estrogen receptor α receptor activation, but not others, contributes to sensitization of nociceptive signaling in trigeminal sensory neurons.

An explainable machine learning framework for lung cancer hospital length of stay prediction.

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3-100%, and 97%, CI 95%: 93.7-100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2-59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU.

Decreased tissue stiffness in glioblastoma by MR elastography is associated with increased cerebral blood flow.

PURPOSE: Understanding how mechanical properties relate to functional changes in glioblastomas may help explain different treatment response between patients. The aim of this study was to map differences in biomechanical and functional properties between tumor and healthy tissue, to assess any relationship between them and to study their spatial distribution. METHODS: Ten patients with glioblastoma and 17 healthy subjects were scanned using MR Elastography, perfusion and diffusion MRI. Stiffness and viscosity measurements G' and G'', cerebral blood flow (CBF), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were measured in patients' contrast-enhancing tumor, necrosis, edema, and gray and white matter, and in gray and white matter for healthy subjects. A regression analysis was used to predict CBF as a function of ADC, FA, G' and G''. RESULTS: Median G' and G'' in contrast-enhancing tumor were 13% and 37% lower than in normal-appearing white matter (P < 0.01), and 8% and 6% lower in necrosis than in contrast-enhancing tumor, respectively (P < 0.05). Tumors showed both inter-patient and intra-patient heterogeneity. Measurements approached values in normal-appearing tissue when moving outward from the tumor core, but abnormal tissue properties were still present in regions of normal-appearing tissue. Using both a linear and a random-forest model, prediction of CBF was improved by adding MRE measurements to the model (P < 0.01). CONCLUSIONS: The inclusion of MRE measurements in statistical models helped predict perfusion, with stiffer tissue associated with lower perfusion values.

Conserved oligomeric Golgi (COG) complex genes functioning in defense are expressed in root cells undergoing a defense response to a pathogenic infection and exhibit regulation my MAPKs.

The conserved oligomeric Golgi (COG) complex maintains correct Golgi structure and function during retrograde trafficking. Glycine max has 2 paralogs of each COG gene, with one paralog of each gene family having a defense function to the parasitic nematode Heterodera glycines. Experiments presented here show G. max COG paralogs functioning in defense are expressed specifically in the root cells (syncytia) undergoing the defense response. The expressed defense COG gene COG7-2-b is an alternate splice variant, indicating specific COG variants are important to defense. Transcriptomic experiments examining RNA isolated from COG overexpressing and RNAi roots show some COG genes co-regulate the expression of other COG complex genes. Examining signaling events responsible for COG expression, transcriptomic experiments probing MAPK overexpressing roots show their expression influences the relative transcript abundance of COG genes as compared to controls. COG complex paralogs are shown to be found in plants that are agriculturally relevant on a world-wide scale including Manihot esculenta, Zea mays, Oryza sativa, Triticum aestivum, Hordeum vulgare, Sorghum bicolor, Brassica rapa, Elaes guineensis and Saccharum officinalis and in additional crops significant to U.S. agriculture including Beta vulgaris, Solanum tuberosum, Solanum lycopersicum and Gossypium hirsutum. The analyses provide basic information on COG complex biology, including the coregulation of some COG genes and that MAPKs functioning in defense influence their expression. Furthermore, it appears in G. max and likely other crops that some level of neofunctionalization of the duplicated genes is occurring. The analysis has identified important avenues for future research broadly in plants.

Robustness of MR Elastography in the Healthy Brain: Repeatability, Reliability, and Effect of Different Reconstruction Methods.

BACKGROUND: Changes in brain stiffness can be an important biomarker for neurological disease. Magnetic resonance elastography (MRE) quantifies tissue stiffness, but the results vary between acquisition and reconstruction methods. PURPOSE: To measure MRE repeatability and estimate the effect of different reconstruction methods and varying data quality on estimated brain stiffness. STUDY TYPE: Prospective. SUBJECTS: Fifteen healthy subjects. FIELD STRENGTH/SEQUENCE: 3T MRI, gradient-echo elastography sequence with a 50 Hz vibration frequency. ASSESSMENT: Imaging was performed twice in each subject. Images were reconstructed using a curl-based and a finite-element-model (FEM)-based method. Stiffness was measured in the whole brain, in white matter, and in four cortical and four deep gray matter regions. Repeatability coefficients (RC), intraclass correlation coefficients (ICC), and coefficients of variation (CV) were calculated. MRE data quality was quantified by the ratio between shear waves and compressional waves. STATISTICAL TESTS: Median values with range are presented. Reconstruction methods were compared using paired Wilcoxon signed-rank tests, and Spearman's rank correlation was calculated between MRE data quality and stiffness. Holm-Bonferroni corrections were employed to adjust for multiple comparisons. RESULTS: In the whole brain, CV was 4.3% and 3.8% for the curl and the FEM reconstruction, respectively, with 4.0-12.8% for subregions. Whole-brain ICC was 0.60-0.74, ranging from 0.20 to 0.89 in different regions. RC for the whole brain was 0.14 kPa and 0.17 kPa for the curl and FEM methods, respectively. FEM reconstruction resulted in 39% higher stiffness than the curl reconstruction (P < 0.05). MRE data quality, defined as shear-compression wave ratio, was higher in peripheral regions than in central regions of the brain (P < 0.05). No significant correlations were observed between MRE data quality and stiffness estimates. DATA CONCLUSION: MRE of the human brain is a robust technique in terms of repeatability. Caution is warranted when comparing stiffness values obtained with different techniques. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Simulation and Measurement of Aerosolisation in Different Chest Drainage Systems.

The aim of the study was to assess the degree of aerosolisation in different chest drainage systems according to different air leak volumes, in a simulated environment. This novel simulation model was designed to produce an air leak by passing air through and agitating a fluorescent fluid. The air leak volume and amount of fluorescent fluid were tested in various combinations and aerosolisation was assessed at 10-minute intervals using the ultraviolet light. The following chest drainage systems were compared: (1) single-chamber chest drainage system, (2) 3-compartment wet-dry suction chest drainage system, (3) digital drainage and monitoring system. The impact of suction (-2 and -4 kPa) in generating aerosolised particles was tested as well. A total number of 187 of 10-minute interval measurements were performed. The single-chamber chest drainage system generated the largest number of aerosolised particles at different air leak volumes and drainage output. The 3-compartment wet-dry suction system and the digital drainage and monitoring system did not generate any identifiable aerosolised particles at any of the air leak or drain output volumes considered. Suction applied to the chest drainage systems did not have an effect on aerosolisation. Aerosol generation in the simulated air-leak model demonstrated the potential risk of SARS-CoV-2 spread in the clinical setting. Full personal protective equipment must be used in patients with an air leak. Single-chamber chest drainage system generates the highest rate of aerosolised particles and it should not be used as an open system in patients with an air leak.

Mitogen activated protein kinase (MAPK)-regulated genes with predicted signal peptides function in the Glycine max defense response to the root pathogenic nematode Heterodera glycines.

Glycine max has 32 mitogen activated protein kinases (MAPKs), nine of them exhibiting defense functions (defense MAPKs) to the plant parasitic nematode Heterodera glycines. RNA seq analyses of transgenic G. max lines overexpressing (OE) each defense MAPK has led to the identification of 309 genes that are increased in their relative transcript abundance by all 9 defense MAPKs. Here, 71 of those genes are shown to also have measurable amounts of transcript in H. glycines-induced nurse cells (syncytia) produced in the root that are undergoing a defense response. The 71 genes have been grouped into 7 types, based on their expression profile. Among the 71 genes are 8 putatively-secreted proteins that include a galactose mutarotase-like protein, pollen Ole e 1 allergen and extensin protein, endomembrane protein 70 protein, O-glycosyl hydrolase 17 protein, glycosyl hydrolase 32 protein, FASCICLIN-like arabinogalactan protein 17 precursor, secreted peroxidase and a pathogenesis-related thaumatin protein. Functional transgenic analyses of all 8 of these candidate defense genes that employ their overexpression and RNA interference (RNAi) demonstrate they have a role in defense. Overexpression experiments that increase the relative transcript abundance of the candidate defense gene reduces the ability that the plant parasitic nematode Heterodera glycines has in completing its life cycle while, in contrast, RNAi of these genes leads to an increase in parasitism. The results provide a genomic analysis of the importance of MAPK signaling in relation to the secretion apparatus during the defense process defense in the G. max-H. glycines pathosystem and identify additional targets for future studies.

Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach.

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the length of stay (LOS) predictive analytics for cardiovascular inpatients in ICU. However, there are almost scarcely real attempts utilized machine learning models to predict the likelihood of heart failure patients length of stay in ICU hospitalization. This paper introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art- machine learning models, in particular, the ensembles regressors and deep learning regression models. Our results showed that the gradient boosting regressor (GBR) outweighed the other proposed models in this study. The GBR reported higher R-squared value followed by the proposed method in this study called Staking Regressor. Additionally, The Random forest Regressor (RFR) was the fastest model to train. Our outcomes suggested that deep learning-based regressor did not achieve better results than the traditional regression model in this study. This work contributes to the field of predictive modelling for electronic medical records for hospital management systems.

Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications.

Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies.

Diagnosis of Acute Heart Failure Using Inferior Vena Cava Ultrasound: Systematic Review and Meta-analysis.

OBJECTIVES: The utility of bedside inferior vena cava (IVC) ultrasound (US) in the diagnosis of heart failure (HF) is unclear. The aim of this study was to determine whether IVC parameters in patients with acute heart failure (AHF) are statistically different from those without HF. METHODS: The MEDLINE database of English-language publications from 1966 to August 2018 was searched. Retrospective and prospective studies that included either IVC expiratory diameter (IVCexp ) or IVC collapsibility index (IVC-CI) values were collected in patients with and without HF. to determine whether there was a statistical difference in the IVC parameters between these groups. RESULTS: A total of 27 articles with a total of 1472 patients with AHF were included. The standard mean differences for the IVCexp and IVC-CI for the control group versus the AHF group were found to be statistically significant (P < .0001). The combined mean IVCexp values were 15.11 mm (95% confidence interval [CI], 14.19-16.02 mm) for the control group and 20.26 mm (95% CI, 14.82-25.71 mm) for the AHF group. The combined mean IVC-CI values were 61.6% (95% CI, 48.4%-74.7%) for the control group and 30.5% (95% CI, 26.4%-34.6%) for the AHF group. CONCLUSIONS: Bedside IVC US showed that a statistically significant difference existed in the IVC parameters between patients with and without AHF. Based on mean calculations, an IVCexp of greater than 2.0 cm and an IVC-CI of less than 30% are reasonable cutoffs to suggest that a patient with acute dyspnea is more likely to have AHF than a non-AHF condition. Given the high degree of heterogeneity across the studies and the high risk of bias, larger randomized studies are warranted to explore the use of IVC US in patients with HF.